Supporting data for “Developing algorithms to optimize recommender system for self-regulated learning promotion in online contexts: a design-based study” by Long Zhang (11129139)

The time intervals of 3 different stations and 4 different data sets analysed with the developed algorithm. Compared with Karaman Radar data time range and with each other. by Ekim KÜLÜM (20846213)

“…<p>The time intervals of 3 different stations and 4 different data sets analysed with the developed algorithm. Compared with Karaman Radar data time range and with each other.…”

Evaluating the robustness of GNNs compared to benchmarks (A) and their required training time (B). by Daniel Walke (21680915)

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Comparing AUROC across several deep learning techniques for the classification of sepsis on complete blood count data (higher values represent better performance). We evaluated the... by Daniel Walke (21680915)

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Comparing AUROC across Graph Neural Networks patient-centric graphs (directed, reversed directed and undirected) for the classification of sepsis on complete blood count data (high... by Daniel Walke (21680915)

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Comparison of GNNs against benchmarks on complete blood count data for sepsis classification (higher values represent better performance) and their required training time. We evalu... by Daniel Walke (21680915)

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Complete blood counts of a single patient by Daniel Walke (21680915)

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Workflow of our study to evaluate the performance of GNNs compared to benchmark algorithms. by Daniel Walke (21680915)

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Partial dependence plots (A – G) and the resulting clustered feature importance (H) for each feature and trained model. by Daniel Walke (21680915)

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Comparing AUROC, exploited biases, use cases and issues of Graph Attention Networks on similarity and patient-centric graphs (directed, reversed directed and undirected) for the cl... by Daniel Walke (21680915)

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Agreements and disagreements between scores obtained during items 18, 19 and 22 completion using the Paper-based support (P-score) or the Tablet (T-score). by Dominique Vincent-Genod (20552594)

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Bangdiwala’s agreement charts for item 18 (A), 19 (B) and 22 (C) comparing item scores between the Paper-based support and the Tablet. by Dominique Vincent-Genod (20552594)

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Agreements and disagreements between scores of the therapist’s score on the tablet (T-score) and the automatic score (A-score) from the TabMe2 software. by Dominique Vincent-Genod (20552594)

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Subject characteristics. by Dominique Vincent-Genod (20552594)

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Original paper-based support for item 18 (A), 19 (B) and 22 (C) and screenshots of the digital interfaces of the item 18 (D), 19 (E) and 22 (F). by Dominique Vincent-Genod (20552594)

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List of meaningful comments reported by study subjects and therapists after item completion 18, 19 and 22. by Dominique Vincent-Genod (20552594)

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Assessment of the level of difficulty of the task from subjects (A) and therapists (B) when using the tablet compared to the paper-based support during the item 18, 19 and 22 compl... by Dominique Vincent-Genod (20552594)

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Research data. by Dominique Vincent-Genod (20552594)

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Percentage agreement and weighted Kappa coefficient between evaluation using the paper-based support or the tablet for item 18, 19 and 22. by Dominique Vincent-Genod (20552594)

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Comparison of OCT-derived total skin thickness at baseline and at 7 months. Total skin thickness was measured using OCT at baseline and at 7 months to assess changes over time. The... by Anna V. Vejlsby (22033236)

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